JISC's Learning Registry Node Experiment at Mimas

Archive for the tag “linked data”

The Wider Potential report (121109 JLeRN Wider Potential Report – DK) introduced here presents personal but hopefully useful observations on the Learning Registry as a work in progress. The report considers the wider potential and affordances of the Learning Registry as an architecture or conceptual approach, looking beyond its core educational technology focus to the broader information environment. Inevitably, this report only scratches the surface and should therefore be weighed against the more detailed inputs (covering both technology and practice) to the JISC Learning Registry Node (JLeRN) project.

1 – Status

In organizational terms, the LR is only a time-limited project and therefore its potential has to be realized beyond those boundaries; however, it is already valuable that practitioners intuitively recognise both the relevance of its approach and the possibilities it may open up.

In solution terms, the LR is only a configuration of IT plumbing, of machines talking to machines (that’s all it set out to be); therefore it is down to the community (self-selecting) to build both local and larger scale services and to enhance the range of interfaces (APIs) and the scope of value-added applications. Such engagement might be more readily achieved if the potential of two key aspects were to be more clearly demonstrated – how paradata might work at scale given the nature of the underlying identifiers and how the networked node model could serve a range of practical use cases.

In technology terms, the LR made some choices at a moment in time (e.g. to use the Couch noSQL database). Whilst these are far from out-dated, it may be that it is the approach that is more significant going forward than the specific architecture, tools or code.

In terms of vision, the LR appears to have catalysed encouraging levels of interest in three constituencies – policy makers (especially in US K-12 education), learning technologists and, perhaps most refreshingly, parties responsible for delivery (such as the Liverpool and Newcastle University teams engaged in the UK JLeRN investigation). Whilst enthusiasms and movements are dangerous, it is not insignificant when an approach captures imaginations in an embattled landscape such as Learning Resource description, discovery and reuse.

2 – Crossroads

At this point in the story, as initial project funding comes to an end, we are however faced with a familiar ‘investment’ dilemma (whether about effort or funds) concerning the tensions between ‘forever beta’ rapid innovation (technology and tools are always moving on) and the challenges of embedding in the community and of reliable productisation. Survival in this technology ‘gene pool’ is a complex proposition. The key sustainability questions are:

In product terms, does the LR add enough to the underlying technology stack to establish a necessary and valued role? Or is the LR simply an exemplification of what can be achieved using increasingly malleable lower level components?

In terms of engagement, are the educational audience too narrow and the post-project governance too uncertain to elicit the ongoing commitment needed to deliver the power of the LR approach? Or is there a wider value in the LR approach potentially involving other domains that would bring critical mass and a sustainable trajectory?

3 – Recommendations

It is perhaps unhelpful in the current funding climate to propose further work. It seems certain however that neither the benefits to the leaning community (with the possible exception of specific US K-12 targets) nor any wider /generic potential of the LR approach can be achieved without further proof of concept around its potentially groundbreaking features – notably harnessing paradata and offering a node based data aggregation model.

To market the current ‘solution’ to the wider learning community or to other information domains, such as libraries and estates in HE, without clarity in those areas would likely be fruitless. Technologists would justifiably resort to the underlying toolset (notably the power of CouchDB) and practitioners would be left, as we are, to imagine outcomes on a half-promise.

I for one would suggest that low budget and rapidly executed proof of concept experiments could be devised around paradata and the node model that would get us to a more tangible decision point regarding value to the teaching and learning community and wider affordances. These need to take place urgently before we loose track of achievements to date.

Ironically, talking of wider potential, the library community and particularly aggregations such as the Mimas-managed Copac service (http://copac.ac.uk/about/) have use cases and data to support both investigations. Furthermore the JISC Activity Data programme (http://activitydata.org) and at least one very large ongoing European project (Open Discovery Service running to 2015 – http://opendiscoveryspace.eu/project.html) may be poised to address mutually interesting requirements in this space.

Finally, as a sanity check, it may be of value to undertake an analysis of the space addressed by the LR based on the California Digital Library micro-services approach (https://confluence.ucop.edu/display/Curation/Microservices) in order to determine the necessary working parts and how they might be sourced, without the presumption of building and maintaining a single end-to-end system.

I’ve recently had a number of interesting and informative conversations in my attempts to pin down the nature of the Learning Registry (LR) and its potential significance within and beyond its US origins. I thought it might be interesting to summarise some of the headline ‘findings’ ahead of the Mimas JLeRN workshop on 22nd October, all of which are of course subject to learning more on the day. These are best presented as a sort of historical narrative …

2) Whilst those challenges may have had particular priority in the minds of DoD and DoE stakeholders, they were symptomatic of the much-discussed difficulties of describing learning resources in manners that will be enable potential uses and users (from course designers to learners) elsewhere; to put it bluntly there is no consensus after all these years that enables us to homogenize / harmonize learning resource metadata – it’s like a muddy pond containing fish, plant life, shopping trolleys, industrial byproducts, children swimming, others fishing … it feels like a random ‘mess’ not an ecosystem.

3) Paradata (i.e. usage data with context) may be a vital part of the jigsaw – allowing resources to become increasingly ‘well-described’ on the basis of their utilization (Who, Where, When, How, etc…); however it is only a format that is subject to the quality of the data itself, particularly re- the use (or not of) consistent and persistent identifiers to ‘link’ paradata.

4) Paradata has the potential to be more powerful at scale (introducing statistical reliability and exposing the long tail of resources and of usage) and may therefore benefit from the ability to network datasets across the community (subject, national, international).

5) The LR project developed an ‘approach’ (model, architecture…) that addresses the key issues of mess (see 2), context (see 3) and scale (see 4). In my simple terms, the LR approach proposes that the mess is addressed by a flexible approach to data attributes (anything goes), context is evidenced by paradata, and scale is enabled by orchestration between networked nodes.

6) In organizational and temporal terms, LR is only a project and therefore the potential has yet to be realized; however, it is already valuable that practitioners intuitively recognise both the relevance of this response and the possibilities it may open up.

7) In solution terms, LR is only a configuration of plumbing, of machines talking to machines (that’s all it set out to be) and therefore it is emphatically down to the community (who’s that?) to build both local and larger scale services, interfaces and applications on top.

8) In technology terms, LR made some choices at a moment in time (e.g. to use the Couch noSQL database); whilst these are far from out-dated, it may be that it is the approach that is more significant going forward than the architecture or the code.

9) The potential of learning paradata raises issues about the divergence (or is it convergence, glass half full?) of approaches to usage data / activity streams; in one dimension this data represents part of the personal learning record (“I did this”), whilst ‘at the other end of the triple’ (thanks, Phil) it is about the resource (“It was used in this way”); that sounds exciting till we dig deeper in to issues of storage / retrieval, privacy / access and more.

10) At this point in the history (Is that an end point? I think it was Simon Schama who asserted that the French Revolution is still ongoing), we are faced with a familiar dilemma concerning the investment relationship between rapid innovation (technology and tools are always moving on), embedding in the community and reliable productisation … Is the target audience too narrow and the governance too uncertain to deliver the power of the LR approach? Is there a wider value in the LR approach that would bring critical mass and a sustainable trajectory?